Fall detection of human beings is an intensifying, unmet need around the world, propelled by burgeoning populations of older individuals. Fall detection is a growing problem for disabled individuals as well—for example, those who are afflicted by epilepsy, juvenile diabetes, muscular dystrophy, multiple sclerosis, narcolepsy, or other conditions which result in sudden unconsciousness or loss of motor control.
At present, most commercially-available systems for fall detection involve a device that must be worn by the end user. Such wearables are usually in the form of either a necklace or a bracelet (worn directly), or a mobile phone (worn in the pocket). Wearable fall-detection mechanisms fall into two classes—manual devices, wherein the user must press a button on the device to call for help, and automatic devices, wherein the device includes an accelerometer and calls for help automatically when a sudden downward movement is detected.
Wearable monitoring devices suffer from various drawbacks. For example, many eligible users resist wearing fall-detection devices due to the stigma associated with the use of such devices. Many wearables are conspicuous, constant reminders of the user's lack of independence. As a result of such psychological resistance, the adoption of wearable sensors is estimated to be only 5-10% of individuals who would otherwise benefit from fall detection.
Wearable monitoring devices are also inconvenient. In order to be effective, they must always be worn, and the wearer must remember that he/she is wearing the device if it requires an action such as the press of a button upon the occurrence of an adverse event. Thus, certain devices are unsuitable for certain cognitively-impaired users.
Wearables may also suffer from poor accuracy. Manual devices often end up trapped under the body of a fallen individual, placing the call-for-help button out of reach of the user upon the occurrence of an adverse event such as a fall, adverse cardiac event, or other medical emergency. Automatic devices may fail to register an actual fall, e.g., by incorrectly interpreting the user as sitting down, or the converse. Estimates of wearable fall-detector accuracy generally top out at 80%, and are typically lower.
Because of these problems, various monitoring systems that do not have a wearable component (e.g., ‘touchless’ systems) have been proposed. However, these systems have various drawbacks as well.
For example, systems with an audio sensor for detection of the noise of a fall, or a human cry for help, are complex to set up and calibrate for use by a given individual, and real-world accuracy is low. Environmental noise, such as the sound made by televisions, human conversation, outdoor noises, storms, and other events may lead to inaccuracy, including false positives and false negatives. Furthermore, certain adverse events, e.g., certain falls or medical emergencies, are not accompanied by noise, or may not register, depending on the location the event occurs.
Systems that detect certain vibration may include sensors installed in or on the floor, or under the carpet, to detect vibrations that are associated with a fall. These systems may be difficult to set up, they may be expensive, and there are accuracy problems.
Various video sensors have been proposed, but these suffer from drawbacks as well. Most inexpensive motion sensors are not sophisticated enough to detect a fall or other adverse event, and they may be difficult to install. Depth-based sensors for fall detection, for example, infrared-based systems, may be expensive and may operate in a limited range. Current multiple camera systems may be complex to set up, expensive, and pose privacy concerns. Many systems of this kind require manual human verification of detected events. Current single-camera video systems transmit video footage to a monitoring center, or may be accessed by a loved one or caretaker of an individual being monitored. Various video analysis software has been developed in a laboratory setting, but these algorithms are complex and adaptation to real-word environments is questionable. For example, freely-placed camera systems encounter different room shapes and sizes, different placement heights and angular orientations, different backgrounds and light conditions, different furniture configurations, which vary by user as well as temporally. Algorithms that compensate for these variations are complex and do not perform well when used to detect real-world falls.
Thus, current systems are difficult, expensive, time-consuming to install, configure, and maintain. They may store or disseminate images or video, possibly compromising individual privacy. They may require form factors or high-end computational power that is expensive or physically large and therefore difficult to deploy in a home environment. Existing systems also lack accuracy and may require substantial human operation or involvement. Furthermore, existing systems may suffer from lack of compliance due to complexity, inconvenience, or stigma.
There is a need for non-obtrusive, privacy-preserving methods and systems for automatic detection of a behavior of an individual such as a fall or adverse medical event, suitable for in-home use, with improved accuracy, ease of use, and low expense.